A multi-mode electric vehicle range estimator based on driving pattern recognition

Mao, Lang; Fotouhi, Abbas; Shateri, Neda; Ewin, Nathan

doi:10.1177/09544062211032994

Cited by 13 publications

(4 citation statements)

References 45 publications

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“…However, such EVs face some issues when in use on the road [15]. Major problems associated with the EVs while riding are short-range [18], higher weight [19], and bulkiness of the energy storage systems such as battery packs hence lowering the utility space [20], whole-body vibrations [21,22], torque ripple [23], and torsion between traction motor and driveline [24]. Any successful attempt made in overcoming or minimizing the aforesaid deficiencies would result in improving the efficiency and performance of futuristic EVs.…”

Section: Ice Motorcyclementioning

confidence: 99%

A Review on Vibrations in Electric and Hybrid Electric Vehicles

Krishna

Mahesha

Hegde

et al. 2023

J. Inst. Eng. India Ser. C

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Electric vehicles (EVs) are promising solutions to the problems faced by combustion engine-propelled automobiles. The futuristic transportation system would be majorly catered by battery EVs, hybrid EVs, and fuel cell EVs with a different combination of energy sources that could successfully meet the requirements of different categories of vehicle propulsion systems. However, to execute such vehicle systems with a high level of safety and comfort for the transportation of people and products, a few difficulties must be resolved. EVs of today face problems such as noise-vibration-harshness, energy storage difficulties, torque fluctuation/ripple in the power train, inadequate range, and some others. The present review paper comprehends major noise and vibration issues existing in EVs and attempts made by the researchers to find solutions to such problems. The major problem faced by the vehicle as well as by the passengers is vibration and discomfort. These vibrations if continued for a longer period might cause severe damages to the vehicle structure as well as to the passenger’s body. These issues must be thoroughly addressed by researchers to increase ride comfort, which is a crucial requirement of the hour as well.

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Section: Ice Motorcyclementioning

confidence: 99%

A Review on Vibrations in Electric and Hybrid Electric Vehicles

Krishna

Mahesha

Hegde

et al. 2023

J. Inst. Eng. India Ser. C

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show abstract

“…Web of Science and Engineering Village (Publisher: MDPI, Elsevier, IEEE, etc.) energy management method fuel cell vehicles driving cycle information driving cycle collection driving pattern recognition driving pattern recognition website report (1) review papers (18) journal articles (118) real-world driving cycle information (60), mainly from China non-real-world driving cycle information (58)…”

Section: Scope Abstract Resultsmentioning

confidence: 99%

“…In light of this, accurate and effective driving pattern recognition (DPR) can provide positive guidance for the development and design of EMMs. Some studies adopted clustering [59,60] and a fuzzy controller [61,62] to establish the driving pattern recognizer. However, the DPR effect of the clustering method is related to the initial point selection, and it is easy to fall into the local optimum.…”

Section: Driving Pattern Recognitionmentioning

confidence: 99%

Review of Energy Management Methods for Fuel Cell Vehicles: From the Perspective of Driving Cycle Information

Wang,

Hao,

et al. 2023

Sensors

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Energy management methods (EMMs) utilizing sensing, communication, and networking technologies appear to be one of the most promising directions for energy saving and environmental protection of fuel cell vehicles (FCVs). In real-world driving situations, EMMs based on driving cycle information are critical for FCVs and have been extensively studied. The collection and processing of driving cycle information is a fundamental and critical work that cannot be separated from sensors, global positioning system (GPS), vehicle-to-vehicle (V2V), vehicle-to-everything (V2X), intelligent transportation system (ITS) and some processing algorithms. However, no reviews have comprehensively summarized the EMMs for FCVs from the perspective of driving cycle information. Motivated by the literature gap, this paper provides a state-of-the-art understanding of EMMs for FCVs from the perspective of driving cycle information, including a detailed description for driving cycle information analysis, and a comprehensive summary of the latest EMMs for FCVs, with a focus on EMMs based on driving pattern recognition (DPR) and driving characteristic prediction (DCP). Based on the above analysis, an in-depth presentation of the highlights and prospects is provided for the realization of high-performance EMMs for FCVs in real-world driving situations. This paper aims at helping the relevant researchers develop suitable and efficient EMMs for FCVs using driving cycle information.

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“…In general, the ordinary EVs display a metric related to the residual driving range named a distance to empty (DTE), remaining distance, remaining driving range, etc. The research for EV range prediction has been studied by considering physics-based modeling and test-based performance map (Ferreira, Monteiro, and Afonso 2013;Oliva, Weihrauch, and Bertram 2013;Miri, Fotouhi, and Ewin 2021) as well as by utilizing regression-based parameters estimation and machine learning techniques Ondruska and Posner 2014;Fetene et al 2017;Mao et al 2021;Sun et al 2019;Topic, Skugor, and Deur 2019;Warey et al 2020;Zhao et al 2020).…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Method for EV Range Prediction with Updates on Route Information and Traffic Conditions

Kim

Shim

2022

AAAI

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Driver's anxiety about the remaining driving range of electric vehicles (EVs) has been quite improved by mounting a high-capacity battery pack. However, when EVs need to be charged, the drivers still feel uncomfortable if inaccurate range prediction is provided because the inaccuracy makes it difficult to decide when and where to charge EV. In this paper, to mitigate the EV range anxiety, a new machine learning (ML) method to enhance range prediction accuracy is proposed in a practical way. For continuously obtaining the recent traffic conditions ahead, input features indicating the near-future vehicle dynamics are connected to a long short-term memory (LSTM) network, which can consecutively utilize a relation of neighboring data, and then the output features of the LSTM network with another input features consisting of energy-related vehicle system states become another input layer for deep learning network (DNN). The proposed LSTM-DNN mixture model is trained by exploiting the driving data of about 160,000 km and the following test performance shows that the model retains the range prediction accuracy of 2 ~ 3 km in a time window of 40 min. The test results indicate that the LSTM-DNN range prediction model is able to make a far-sighted range prediction while considering varying map and traffic information to a destination.

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A multi-mode electric vehicle range estimator based on driving pattern recognition

Cited by 13 publications

References 45 publications

A Review on Vibrations in Electric and Hybrid Electric Vehicles

A Review on Vibrations in Electric and Hybrid Electric Vehicles

Review of Energy Management Methods for Fuel Cell Vehicles: From the Perspective of Driving Cycle Information

A Machine Learning Method for EV Range Prediction with Updates on Route Information and Traffic Conditions

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